Data Engineer , Amazon Customer Service

As Data Engineer on the Amazon Customer Service Data Intelligence team, you will design and implement robust data pipelines and infrastructure that power our organization's data-driven decisions and AI capabilities. This role is critical in developing and maintaining our enterprise-scale data processing systems that handle high-volume transactions while ensuring data security, privacy compliance, and optimal performance. You'll be part of a dynamic team that designs and implements comprehensive data solutions, from real-time processing architectures to secure storage solutions and privacy-compliant data access layers. The role involves close collaboration with cross-functional teams, including software development engineers, product managers and scientists, to create data products that power critical business capabilities. You'll have the opportunity to work with leading technologies in cloud computing, big data processing, and machine learning infrastructure, while contributing to the development of robust data governance frameworks. If you're passionate about solving complex technical challenges in high-scale environments, thrive in a collaborative team setting, and want to make a lasting impact on our organization's data infrastructure, this role offers an exciting opportunity to shape the future of our data and AI capabilities. Key job responsibilities - Design and implement ETL/ELT frameworks that handle large-scale data operations, while building reusable components for data ingestion, transformation, and orchestration while ensuring data quality and reliability. - Establish and maintain robust data governance standards by implementing comprehensive security controls, access management frameworks, and privacy-compliant architectures that safeguard sensitive information. - Drive the implementation of data solutions, both real-time and batch, optimizing them for both analytical workloads and AI/ML applications. - Lead technical design reviews and provide mentorship on data engineering best practices, identifying opportunities for architectural improvements and guiding the implementation of enhanced solutions. - Build data quality frameworks with robust monitoring systems and validation processes to ensure data accuracy and reliability throughout the data lifecycle. - Drive continuous improvement initiatives by evaluating and implementing new technologies and methodologies that enhance data infrastructure capabilities and operational efficiency. A day in the life The day often begins with a team huddle to align priorities, followed by a review of data pipeline monitoring alarms to address any processing issues and ensure data quality standards are maintained across systems. Throughout the day, you'll find yourself immersed in various technical tasks, including developing and optimizing ETL/ELT processes, implementing data governance controls, and reviewing code for data processing systems. You'll work closely with software engineers, scientists, and product managers, participating in technical design discussions and sharing your expertise in data architecture and engineering best practices. Your responsibilities extend to communicating with non-technical stakeholders, explaining data-related projects and their business impact. You'll also mentor junior engineers and contribute to maintaining comprehensive technical documentation. You'll troubleshoot issues that arise in the data infrastructure, optimize the performance of data pipelines, and ensure data security and compliance with relevant regulations. Staying updated on the latest data engineering technologies and best practices is crucial, as you'll be expected to incorporate new learnings into your work. By the end of a typical day, you'll have advanced key data infrastructure initiatives, solved complex technical challenges, and improved the reliability, efficiency, and security of data systems. Whether it's implementing new data governance controls, optimizing data processing workflows, or enhancing data platforms to support new AI models, your work directly impacts the organization's ability to leverage data for critical business decisions and AI capabilities. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The Data Intelligence team is a new function within Amazon Customer Service, a global, cross-functional organization of applied scientists, data scientists, economists, software engineers, data engineers, and technical product managers dedicated to advancing AI capabilities and creating meaningful impact for customers. We build data solutions and contextual intelligence using Generative AI, Machine Learning, Natural Language Processing, Ontology, Agentic AI, Multi-Agentic AI Architectures, Reinforcement Learning, Knowledge Graphs, and Model Context Protocol. We move with urgency, build for the long term, and prioritize getting customer experience right, because our work is only successful when it meaningfully improves outcomes for customers Internal job description Minimum qualifications - 3+ years of data engineering experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using SQL experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using Oracle experience Preferred qualifications - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions - Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases) - Experience with data modeling, warehousing and building ETL pipelines Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits . USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually